Individual Database Analysis Report¶

Project: Digital Teotihuacan Mapping Project (TMP) - Phase 1

Objective: This notebook provides a comprehensive analysis of a single legacy or benchmark database. It loads the raw metric files generated by the 02_run_profiling_pipeline.py script and produces a series of tables and visualizations to assess the database's structure, health, data content, and performance.

Instructions:

  1. Set the DATABASE_NAME in the configuration cell below.
  2. Execute all cells from top to bottom (Kernel > Restart & Run All).
  3. Review the outputs and complete the Analyst Summary section at the end.

1. Setup and Configuration¶

In [1]:
import json
import pandas as pd
from pathlib import Path
from IPython.display import display, Markdown, SVG
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# --- CONFIGURATION ---------------------------------------------------
# SET THIS VARIABLE to the name of the database you want to analyze.
# e.g., 'TMP_DF8', 'TMP_DF9', 'tmp_benchmark_wide_numeric', etc.
DATABASE_NAME = "TMP_DF10" # <--- CHANGE THIS
# ---------------------------------------------------------------------

# --- Path Definitions ---
# Use relative paths from the notebook's location in reports/individual_db_analysis/
METRICS_DIR = Path("../../outputs/metrics")
ERDS_DIR = Path("../../outputs/erds")

# --- Styling and Display Options ---
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 120)

def display_header(title):
    display(Markdown(f'### {title}'))

def load_metric_file(metric_name, file_type='csv'):
    """Helper function to safely load a metric file."""
    file_path = METRICS_DIR / f"{DATABASE_NAME}_{metric_name}.{file_type}"
    if not file_path.exists():
        print(f"⚠️ WARNING: Metric file not found: {file_path.name}")
        return None
    if file_type == 'csv':
        return pd.read_csv(file_path)
    elif file_type == 'json':
        with open(file_path, 'r') as f:
            return json.load(f)

print(f"✅ Setup complete. Analyzing database: '{DATABASE_NAME}'")
print(f"Metrics Directory: {METRICS_DIR}")
print(f"ERD Directory: {ERDS_DIR}")

✅ Setup complete. Analyzing database: 'TMP_DF10'
Metrics Directory: ..\..\outputs\metrics
ERD Directory: ..\..\outputs\erds

2. Data Loading¶

Load all available metric files for the selected database. Warnings will be printed for any missing files.

In [2]:
# Load all metric files into variables
basic_metrics = load_metric_file('basic_metrics', 'json')
schema_counts = load_metric_file('schema_counts', 'json')
interop_metrics = load_metric_file('interop_metrics', 'json')

# Load table metrics and convert to DataFrame
table_metrics_data = load_metric_file('table_metrics', 'json')
table_metrics_df = pd.DataFrame(table_metrics_data) if table_metrics_data else None

# Load column structure and convert to DataFrame
column_structure_data = load_metric_file('column_structure', 'json')
column_structure_df = pd.DataFrame(column_structure_data) if column_structure_data else None

# Load column profiles and convert to DataFrame
column_profiles_data = load_metric_file('column_profiles', 'json')
column_profiles_df = pd.DataFrame(column_profiles_data) if column_profiles_data else None

# Performance benchmarks remain as CSV
performance_df = load_metric_file('performance_benchmarks')

print("✅ Data loading complete.")

✅ Data loading complete.

3. High-Level Overview & Schema Visualization¶

In [3]:
display_header(f"Key Metrics for: {DATABASE_NAME}")

summary_data = {}
if basic_metrics: summary_data.update(basic_metrics)
if schema_counts: summary_data.update(schema_counts)
if interop_metrics: summary_data.update(interop_metrics)
if table_metrics_df is not None:
    summary_data['total_estimated_rows'] = int(table_metrics_df['row_estimate'].sum())

if summary_data:
    summary_series = pd.Series(summary_data).rename('Value').to_frame()
    display(summary_series)
else:
    print("No summary metrics available.")

Key Metrics for: TMP_DF10¶

Value
database_name TMP_DF10
database_size_mb 64
schema_name tmp_df10
table_count 9
view_count 0
function_count 0
sequence_count 5
jdi 0.2778
lif 7
nf 0.2485
total_estimated_rows 485797

Entity-Relationship Diagram (ERD)¶

The following diagram shows the full relational structure of the database schema. For complex schemas like tmp_df9, this may be very large and focused ERDs should be consulted.

In [4]:
display_header(f"Full ERD for: {DATABASE_NAME}")

try:
    # Find the most recent ERD file for the database
    erd_files = sorted(ERDS_DIR.glob(f"{DATABASE_NAME}_full_ERD_*.svg"), reverse=True)
    if erd_files:
        display(SVG(erd_files[0]))
    else:
        print(f"❌ ERROR: Full ERD SVG file not found for '{DATABASE_NAME}'.")
except Exception as e:
    print(f"An error occurred while displaying the ERD: {e}")

Full ERD for: TMP_DF10¶

No description has been provided for this image

4. Table-Level Analysis¶

Analysis of the tables within the schema, focusing on size, row count, and health (bloat).

In [5]:
display_header("Table Metrics Summary")

if table_metrics_df is not None and not table_metrics_df.empty:
    display(table_metrics_df.sort_values(by='row_estimate', ascending=False).style.background_gradient(cmap='viridis', subset=['row_estimate', 'bloat_percent']))
else:
    print("No table metrics data available.")

Table Metrics Summary¶

  table_name row_estimate column_count table_size index_size total_size index_count bloat_bytes bloat_percent bloat_size
0 artifactTable 190870 7 9728 kB 15 MB 25 MB 9 5939445.930646 59.620000 5.66 MB
1 codeTable 148579 5 8104 kB 9184 kB 17 MB 5 4623444.946453 55.710000 4.41 MB
2 totalsTable 70798 5 3768 kB 2976 kB 6784 kB 3 2203074.831026 57.100000 2.1 MB
3 interpTable 62865 5 3296 kB 3712 kB 7048 kB 5 1956217.679206 57.960000 1.87 MB
4 archToSSN 7296 4 320 kB 592 kB 944 kB 3 227035.141772 69.290000 0.22 MB
5 provTable 5050 6 256 kB 128 kB 424 kB 1 157144.663644 59.950000 0.15 MB
6 artifactCodes 149 2 8192 bytes 16 kB 32 kB 1 4636.545521 56.600000 0.0 MB
7 codeCodes 107 2 8192 bytes 16 kB 32 kB 1 3329.599804 40.640000 0.0 MB
8 interpCodes 83 2 8192 bytes 16 kB 32 kB 1 2582.773680 31.530000 0.0 MB
In [6]:
display_header("Largest Tables by Total Size and Bloat")

if table_metrics_df is not None and not table_metrics_df.empty:
    # Convert pretty size string to bytes for sorting
    def size_to_bytes(s):
        if not isinstance(s, str): return 0
        num, unit = s.split()
        num = float(num)
        if 'KB' in unit: return num * 1024
        if 'MB' in unit: return num * 1024**2
        if 'GB' in unit: return num * 1024**3
        return num
    
    df_copy = table_metrics_df.copy()
    df_copy['total_bytes'] = df_copy['total_size'].apply(size_to_bytes)
    df_copy['bloat_bytes_val'] = df_copy['bloat_bytes']

    top_10_size = df_copy.nlargest(10, 'total_bytes')
    top_10_bloat = df_copy.nlargest(10, 'bloat_bytes_val')

    # Display tables
    display(Markdown("**Top 10 Tables by Total Size**"))
    display(top_10_size[['table_name', 'total_size', 'row_estimate']].reset_index(drop=True))
    
    display(Markdown("**Top 10 Tables by Bloat Size**"))
    display(top_10_bloat[['table_name', 'bloat_size', 'bloat_percent', 'row_estimate']].reset_index(drop=True))

    # Create subplots
    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=("Top 10 Tables by Total Size", "Top 10 Tables by Bloat Size")
    )

    fig.add_trace(go.Bar(y=top_10_size['table_name'], x=top_10_size['total_bytes'], orientation='h', name='Total Size'), row=1, col=1)
    fig.add_trace(go.Bar(y=top_10_bloat['table_name'], x=top_10_bloat['bloat_bytes_val'], orientation='h', name='Bloat Size'), row=1, col=2)
    
    fig.update_layout(title_text=f'Table Size Analysis for {DATABASE_NAME}', height=500, showlegend=False)
    fig.update_yaxes(autorange="reversed")
    fig.update_xaxes(title_text="Size (Bytes)", row=1, col=1)
    fig.update_xaxes(title_text="Bloat (Bytes)", row=1, col=2)
    fig.show()
else:
    print("No table metrics data available for plotting.")

Largest Tables by Total Size and Bloat¶

Top 10 Tables by Total Size

table_name total_size row_estimate
0 artifactTable 25 MB 190870
1 codeTable 17 MB 148579
2 interpTable 7048 kB 62865
3 totalsTable 6784 kB 70798
4 archToSSN 944 kB 7296
5 provTable 424 kB 5050
6 artifactCodes 32 kB 149
7 codeCodes 32 kB 107
8 interpCodes 32 kB 83

Top 10 Tables by Bloat Size

table_name bloat_size bloat_percent row_estimate
0 artifactTable 5.66 MB 59.62 190870
1 codeTable 4.41 MB 55.71 148579
2 totalsTable 2.1 MB 57.10 70798
3 interpTable 1.87 MB 57.96 62865
4 archToSSN 0.22 MB 69.29 7296
5 provTable 0.15 MB 59.95 5050
6 artifactCodes 0.0 MB 56.60 149
7 codeCodes 0.0 MB 40.64 107
8 interpCodes 0.0 MB 31.53 83

5. Column-Level Analysis¶

A deep dive into the columns, focusing on data types, data completeness (NULL values), and complexity (cardinality).

In [7]:
display_header("Data Type Distribution")

if column_structure_df is not None:
    type_counts = column_structure_df['data_type'].value_counts().reset_index()
    type_counts.columns = ['data_type', 'count']
    
    # Calculate percentages
    type_counts['percentage'] = (type_counts['count'] / type_counts['count'].sum() * 100).round(2)
    
    # Display comprehensive table
    display(Markdown("**Complete Data Type Distribution**"))
    display(type_counts.style.format({'percentage': '{:.2f}%'}))
    
    # Display summary statistics
    display(Markdown("**Data Type Summary**"))
    summary_stats = pd.DataFrame({
        'Total Columns': [type_counts['count'].sum()],
        'Unique Data Types': [len(type_counts)],
        'Most Common Type': [f"{type_counts.iloc[0]['data_type']} ({type_counts.iloc[0]['count']} columns)"],
        'Least Common Type': [f"{type_counts.iloc[-1]['data_type']} ({type_counts.iloc[-1]['count']} columns)"]
    })
    display(summary_stats)
    
    fig = px.bar(type_counts, x='data_type', y='count', title=f'Column Data Type Frequencies in {DATABASE_NAME}',
                 labels={'count': 'Number of Columns', 'data_type': 'Data Type'})
    fig.show()
else:
    print("No column structure data available.")

Data Type Distribution¶

Complete Data Type Distribution

  data_type count percentage
0 smallint 17 44.74%
1 text 13 34.21%
2 integer 7 18.42%
3 double precision 1 2.63%

Data Type Summary

Total Columns Unique Data Types Most Common Type Least Common Type
0 38 4 smallint (17 columns) double precision (1 columns)

Data Completeness: NULL Value Analysis¶

In [8]:
display_header("Top 20 Columns by Percentage of NULL Values")

if column_profiles_df is not None and not column_profiles_df.empty:
    # Ensure we only show columns with NULLs
    null_df = column_profiles_df[column_profiles_df['null_percent'] > 0].copy()
    
    if not null_df.empty:
        # Create a full column identifier for clarity
        null_df['full_column_name'] = null_df['tablename'] + '.' + null_df['column_name']
        
        top_20_nulls = null_df.nlargest(20, 'null_percent')
        
        # Display table
        display(Markdown("**Top 20 Columns with Highest NULL Percentages**"))
        table_display = top_20_nulls[['full_column_name', 'null_percent', 'null_count_estimate', 'row_count_exact']].copy()
        table_display.columns = ['Column', 'NULL %', 'NULL Count', 'Total Rows']
        display(table_display.reset_index(drop=True))
        
        # Display summary statistics
        display(Markdown("**NULL Value Summary**"))
        null_summary = pd.DataFrame({
            'Total Columns Analyzed': [len(column_profiles_df)],
            'Columns with NULLs': [len(null_df)],
            'Columns with 100% NULLs': [len(null_df[null_df['null_percent'] == 100])],
            'Average NULL %': [f"{null_df['null_percent'].mean():.2f}%"],
            'Median NULL %': [f"{null_df['null_percent'].median():.2f}%"]
        })
        display(null_summary)
        
        fig = px.bar(top_20_nulls, 
                     y='full_column_name', 
                     x='null_percent', 
                     orientation='h',
                     title=f'Top 20 Columns by NULL Percentage in {DATABASE_NAME}',
                     labels={'null_percent': 'Percentage of Rows that are NULL (%)', 'full_column_name': 'Column'})
        fig.update_layout(height=600)
        fig.update_yaxes(autorange="reversed")
        fig.show()
    else:
        print("✅ Excellent! No columns with NULL values were found.")
        
        # Still show summary even when no NULLs
        display(Markdown("**NULL Value Summary**"))
        null_summary = pd.DataFrame({
            'Total Columns Analyzed': [len(column_profiles_df)],
            'Columns with NULLs': [0],
            'Data Completeness': ['100% - Perfect!']
        })
        display(null_summary)
else:
    print("No column profile data available.")

Top 20 Columns by Percentage of NULL Values¶

✅ Excellent! No columns with NULL values were found.

NULL Value Summary

Total Columns Analyzed Columns with NULLs Data Completeness
0 38 0 100% - Perfect!

Data Complexity: Cardinality Analysis¶

Cardinality refers to the number of unique values in a column.

  • Low Cardinality columns (e.g., < 20 unique values) are often categorical codes or flags.
  • High Cardinality columns often represent unique identifiers or free-text fields.
In [9]:
display_header("Column Cardinality Distribution")

if column_profiles_df is not None and not column_profiles_df.empty:
    # Create a full column identifier
    df = column_profiles_df.copy()
    df['full_column_name'] = df['tablename'] + '.' + df['column_name']

    # Display tables of highest and lowest cardinality columns
    display(Markdown("**Columns with Highest Cardinality (Most Unique)**"))
    display(df.nlargest(10, 'distinct_values_estimate')[['full_column_name', 'distinct_values_estimate']])

    display(Markdown("**Columns with Lowest Cardinality (Least Unique)**"))
    display(df[df['distinct_values_estimate'] > 1].nsmallest(10, 'distinct_values_estimate')[['full_column_name', 'distinct_values_estimate']])
    
    # Create a histogram of cardinalities to see the distribution
    fig = px.histogram(df, x='distinct_values_estimate', log_y=True, 
                       title=f'Distribution of Column Cardinalities in {DATABASE_NAME}',
                       labels={'distinct_values_estimate': 'Number of Distinct Values (Cardinality)'})
    fig.show()
else:
    print("No column profile data available.")

Column Cardinality Distribution¶

Columns with Highest Cardinality (Most Unique)

full_column_name distinct_values_estimate
28 interpTable.SSN 5052.0
2 codeTable.SSN 5050.0
34 totalsTable.SSN 5038.0
22 artifactTable.SSN 4997.0
23 artifactTable.Count 933.0
35 totalsTable.Count 562.0
7 provTable.Site 197.0
8 provTable.Subsite 190.0
9 provTable.Unit 142.0
21 artifactTable.ArtCode3 125.0

Columns with Lowest Cardinality (Least Unique)

full_column_name distinct_values_estimate
5 codeTable.Where 2.0
24 artifactTable.Where 2.0
31 interpTable.Where 2.0
37 totalsTable.Where 2.0
19 artifactTable.ArtCode1 7.0
36 totalsTable.Variable 21.0
20 artifactTable.ArtCode2 22.0
30 interpTable.Variable 51.0
4 codeTable.Variable 78.0
29 interpTable.Code 80.0

6. Performance Benchmark Analysis¶

In [10]:
display_header("Canonical Query Performance Results")

if performance_df is not None and not performance_df.empty:
    display(performance_df[['query_name', 'latency_ms', 'status']])
    
    # Plot the results for successful queries
    success_df = performance_df[performance_df['status'] == 'Success']
    if not success_df.empty:
        fig = px.bar(success_df, x='query_name', y='latency_ms',
                     title=f'Query Latency for {DATABASE_NAME}',
                     labels={'latency_ms': 'Latency (ms)', 'query_name': 'Canonical Query'})
        fig.show()
else:
    print("No performance benchmark data available.")

Canonical Query Performance Results¶

query_name latency_ms status
0 Baseline Performance - Query 1.1 1.5392 Success
1 Join_performance Performance - Query 2.1 65.6348 Success
2 Complex_filtering Performance - Query 3.1 8.8834 Success

7. Analyst Summary & Interpretation¶

Instructions: Based on the data and visualizations above, provide a concise summary of your findings for the {DATABASE_NAME} database. Address the following points.

Key Observations:¶

  • Your summary of the most striking features of this database. What is its primary characteristic (e.g., highly normalized, a few very large tables, excellent data completeness)?

Data Quality & Health Concerns:¶

  • Comment on the prevalence of NULL values. Are they concentrated in specific tables or columns? What might this imply about data collection or relevance?
  • Discuss the database bloat. Are there specific tables that are heavily bloated and may require maintenance (e.g., VACUUM FULL)?

Structural Complexity:¶

  • Analyze the relational structure based on the ERD and interoperability metrics (if applicable). Is the schema simple or complex? How does this relate to its JDI/NF score?
  • Discuss the data type usage. Is it appropriate for the data being stored?

Performance Profile:¶

  • Summarize the performance benchmark results. Were queries generally fast or slow? Were there any specific queries that were outliers?

Implications for Phase 2 Redesign:¶

  • Based on this analysis, what are the key strengths and weaknesses of this database's design?
  • What specific aspects of this schema should be preserved, changed, or discarded in the final unified database? (e.g., "The high number of tables needs to be consolidated," or "The use of boolean flags is effective and should be maintained.")